Accountability Model for AI in Engineering: You Cannot Blame the Agent

AI agents can perform engineering tasks but cannot bear accountability, so responsibility for their actions must remain with identifiable people and teams. The proposed model assigns ownership before agents run, defining each agent’s purpose, permissions, autonomy level, required human approvals, escalation contacts, and a rapid shutdown mechanism. Accountability is distributed across service teams, agent platform teams, requesters, and reviewers according to their roles, while every agent-generated change should be linked to a service owner, initiator, and approver. The approach recommends an agent registry, least-privilege access, risk-based approval requirements, standard engineering checks, periodic reviews, and recorded approval trails that capture the agent configuration, request, proposed action, reviewer context, decision, timing, and outcome. By treating AI output like any other engineering contribution, organizations can investigate failures more effectively, improve safeguards after incidents, and use agents without allowing responsibility to become unclear.

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